Deep-learning-based 3D place recognition has received more attention since the datadriven fashion is widely used for the 3D point cloud applications. Most of the existing deep-learning-based 3D place recognition methods only utilise a single scene for place recognition. However, a single scene may have measurement noise or observable dynamic object differences, which may lead to a reduction in recognition accuracy. To improve the performance of 3D place recognition, a sequence matching based rearrangement method is proposed. Our sequence matching method is based on an assignment algorithm and guides the candidate rearrangement in searching for a similar place. The global descriptor extraction adapts the effective sparse tensor representation and a simple pooling layer to obtain the global descriptor. A new loss function combination is employed to train the network. The proposed approach is evaluated on the popular 3D place recognition benchmarks, which proves the effectiveness of the proposed approach.

Light detection and ranging (LiDAR) odometry plays a crucial role in autonomous mobile robots and unmanned ground vehicles (UGVs). This paper presents a deep learning–based odometry system using two successive three-dimensional (3D) point clouds to estimate their scene flow and then predict their relative pose. The network consumes continuous 3D point clouds directly and outputs their scene flow and uncertain mask in a coarse-to-fine fashion. A pose estimation layer without trainable parameters is designed to compute the pose with the scene flow. We also introduce a scan-to-map optimization algorithm to enhance the robustness and accuracy of the system. Our experiments on the KITTI odometry data set and our campus data set demonstrate the effectiveness of the proposed deep learning–based point cloud odometry.